The present invention is related generally to wireless transmission, and in particular, to throughput optimization of mufti-user downlink linear multi-input multi-output (MIMO) precoding systems with quantized feedback.
In MIMO wireless transmission systems, higher rates of throughput (e.g., sum rates) for the overall system are desired. To facilitate gains in throughput, precoding may be used. Precoding is a generalized beamforming scheme for supporting multi-layer transmission in MIMO systems. In precoding, the multiple streams of the signals are emitted from the transmit antennas with independent and appropriate weighting so as to increase the link throughput at the receiver output.
In current systems, precoding algorithms for multi-user MIMO use either linear or nonlinear precoding algorithms. Linear precoding may achieve reasonable performance with lower complexity than nonlinear precoding. Nonlinear precoding may achieve greater capacity, but is very complex and accordingly slower. In general, nonlinear precoding is designed based on the concept of dirty paper coding (DPC). That is, known interference at the transmitter is subtracted without the penalty of resources if the optimal precoding scheme is applied to the transmission signal.
In general, multi user (MU-) MIMO systems offer significant gains over single user (SU-) MIMO schemes. In particular, by increasing the number of users there is a potential gain in system throughput. Even for a relatively small number of users, MU-MIMO systems can still outperform SU-MIMO systems by using an appropriate transmission scheme (such as precoding) for low- and mid-range SNRs.
With full channel state information at the transmitter (CSIT), the capacity of a Gaussian MU-MIMO channel is achieved by using DPC. However, there are insufficient solutions regarding capacity without CSIT or with partial CSIT. Many prior systems have instead used simpler transmission schemes with linear precoding. With partial CSIT, different schemes have been used. For example, a simple random beamforming algorithm has been used to realize the asymptotical gain of scheduling at the limit of large number of users. In another example, a robust opportunistic user scheduling and power allocation strategy based on limited feedback is used. However, these systems have sub-optimal throughput over the network because they do not have full CSIT and cannot quickly and efficiently schedule multiple users.
Therefore, there remains a need to improve throughput in MU-MIMO networks using CSIT.